Matthias Stosiek

Dr. Matthias Stosiek is a Lecturer and researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences, Department of Physics, and the Chair of AI-based Materials Science led by Prof. Dr. Patrick Rinke. His role involves both teaching and advanced research in applying machine learning to materials science and biophysical systems.

Education:

• PhD (Dr. rer. nat.), Universität Regensburg, 2020. Dissertation: "Self-consistent-field ensembles of disordered Hamiltonians: Superconductor-Insulator Transition" under Prof. Dr. Ferdinand Evers.

Research Interests: Dr. Stosiek's research integrates machine learning with materials science and biophysics. He focuses on understanding the structure-property relationships of Lignin-Carbohydrate Complexes (LCCs), a key component in plant biomass. His work includes developing AI-driven models to predict and optimize material properties, contributing to sustainable materials and biorefinery processes. He also explores disordered systems and superconductivity, particularly the superconductor-insulator transition in 2D materials.

Publications Overview: His recent publications (2018–2025) span machine learning applications in materials science, including datasets for LCCs, optimization of polymer actuators, and AI-guided biorefinery processes. Earlier works delve into quantum transport and superconductivity in disordered systems, highlighting his expertise in both fundamental physics and applied AI.

Teaching: Dr. Stosiek co-teaches courses such as "Introduction to Machine Learning for Materials Science 2" and its associated computer tutorials. He also supervises student theses, fostering the next generation of interdisciplinary scientists.

Labs & Teams: He is an integral part of the Chair of AI-based Materials Science, working alongside Prof. Rinke and a multidisciplinary team including Casper Larsen, Xiangzhou Zhu, Nitik Bhatia, and Prajwal Pisal. The group is located at the Garching campus, a hub for cutting-edge research in physics and materials science at TUM.